Tumor Immunity Microenvironment-based classifications of bladder cancer for enhancing cancer immunotherapy

ABSTRACTBackgroundBladder cancer is composed by a mass of heterogenetic characteristics, immunotherapy is a potential way to save the life of bladder cancer patients, but only benefit to about 20% patients.Methods and materialsA total of 4003 bladder cancer patients from 19 cohorts was enrolled in this study, collecting the clinical information and mRNA expression profile. The unsupervised non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithm was used to divide the patients to immune activated, immune exhausted and non-immune class. Verified gene sets of signatures were used to illustrate the characteristic of immunophenotypes. Clinical and genetic features were compared in different immunophenotypes.ResultsWe identified the immune class and non-immune classes in from TCGA-BLCA cohort. The 150 top different expression genes between these two classes was extracted as the input profile for the reappearing of the classification in the other 19 cohorts. As to the activated and exhausted subgroups, a stromal activation signature was conducted by NTP algorithm. Patients in the immune classes shown the highly enriched signatures of immunocytes, while the exhausted subgroup also shown an increased signature of TITR, WNT/TGF-β, TGF-β1 activated, and C-ECM signatures. Patients in the immune activated shown a lower CNA burden, better overall survival, and favorable response to anti-PD-1 therapy.ConclusionWe defined and validated a novel classifier among the 4003 bladder cancer patients. Anti-PD-1 immunotherapy could benefit more for the patients belong to immune activated subgroup, while ICB therapy plus TGF-β inhibitor or EP300 inhibitor might be more effectiveness for patients in immune exhausted subgroup.

Download Full-text

Non-Negative Matrix Factorization for the Analysis of Complex Gene Expression Data: Identification of Clinically Relevant Tumor Subtypes

Cancer Informatics ◽

10.4137/cin.s606 ◽

2008 ◽

Vol 6 ◽

pp. CIN.S606 ◽

Cited By ~ 23

Author(s):

Attila Frigyesi ◽

Mattias Höglund

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Matrix Factorization ◽

Biological Significance ◽

Data Sets ◽

Expression Data ◽

Microarray Expression Data ◽

Tumor Subtypes ◽

Gene Sets ◽

Non Negative Matrix Factorization

Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the obtained metagenes. By analyses of metagenes specific for the same GO-categories we could show that individual metagenes activated different aspects of the same biological processes. Several of the obtained metagenes correlated with tumor subtypes and tumors with characteristic chromosomal translocations, indicating that metagenes may correspond to specific disease entities. Hence, NMF extracts biological relevant structures of microarray expression data and may thus contribute to a deeper understanding of tumor behavior.

Download Full-text

Identification and Validation of A Hypoxia-Related Prognostic and Immune Microenvironment Signature in Bladder Cancer

10.21203/rs.3.rs-155414/v1 ◽

2021 ◽

Author(s):

Xianchao Sun ◽

Ying Zhang ◽

Jinyou Wang ◽

Xiaofeng Zhao ◽

Zhen Zhou ◽

...

Keyword(s):

Bladder Cancer ◽

Cancer Patients ◽

Immune Cell ◽

Immune Escape ◽

Risk Group ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Pcr Analysis ◽

Rna Seq ◽

Immune Microenvironment

Abstract Bladder cancer is the main leading causes of cancer-related deaths and seriously affects population health. Hypoxia plays a key role in tumor development and immune escape, which contributes to malignant behaviors. In this study, we analyzed the RNA-seq and clinical information of bladder cancer patients from The Cancer Genome Atlas (TCGA) database. In order to investigate the hypoxia-related prognostic and immune microenvironment in bladder cancer, we constructed a hypoxia-related risk model for overall survival (OS). Moreover, the RNA-seq and clinical information of bladder cancer patients from Gene Expression Omnibus (GEO) database were used to verified. Our research revealed that the hypoxia risk signature significantly correlated with clinical outcome and independently predict the OS. Furthermore, the hypoxia risk signature could effectively reflect the level of immune cell type fraction and the expression of critical immune checkpoint genes were higher in the high-risk group compare to low-risk group. We also confirmed the expression level of the prognostic genes in bladder cancer and paracancerous tissue samples using qRT-PCR analysis. In summary, the present study identified 7 hypoxia-related genes (HRGs) signature that may be served as an independent clinical predictor and provide a potential mechanism in bladder cancer immunotherapy.

Download Full-text